Co-Design of Anytime Computation and Robust Control
Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X. Nghiem, Joseph Devietti, Rahul Mangharam

Citation
Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X. Nghiem, Joseph Devietti, Rahul Mangharam. "Co-Design of Anytime Computation and Robust Control". 2015 IEEE Real-Time Systems Symposium (RTSS), December 2015.

Abstract
Control software of autonomous robots has stringent real-time requirements that must be met to achieve the control objectives. One source of variability in the performance of a control system is the execution time and accuracy of the state estimator that provides the controller with state information. This estimator is typically perception-based (e.g., Computer Vision-based) and is computationally expensive. When the computational resources of the hardware platform become overloaded, the estimation delay can compromise control performance and even stability. In this paper, we define a framework for co-designing anytime estimation and control algorithms, in a manner that accounts for implementation issues like delays and inaccuracies. We construct an anytime perception-based estimator from standard off-the-shelf Computer Vision algorithms, and show how to obtain a trade-off curve for its delay vs estimate error behavior. We use this anytime estimator in a controller that can use this trade- off curve at runtime to achieve its control objectives at a reduced energy cost. When the estimation delay is too large for correct operation, we provide an optimal manner in which the controller can use this curve to reduce estimation delay at the cost of higher inaccuracy, all the while guaranteeing basic objectives are met. We illustrate our approach on an autonomous hexrotor and demonstrate its advantage over a system that does not exploit co-design.

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Citation formats

• HTML

  Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X.
  Nghiem, Joseph Devietti, Rahul Mangharam. <a
  href="http://www.terraswarm.org/pubs/591.html"
  >Co-Design of Anytime Computation and Robust
  Control</a>, <i>2015 IEEE Real-Time Systems
  Symposium (RTSS)</i>, December 2015.

• Plain text

  Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X.
  Nghiem, Joseph Devietti, Rahul Mangharam. "Co-Design of
  Anytime Computation and Robust Control". <i>2015
  IEEE Real-Time Systems Symposium (RTSS)</i>, December
  2015.

• BibTeX

  @article{PantMohtaAbbasNghiemDeviettiMangharam15_CoDesignOfAnytimeComputationRobustControl,
      author = {Yash Vardhan Pant and Kartik Mohta and Houssam
                Abbas and Truong X. Nghiem and Joseph Devietti and
                Rahul Mangharam},
      title = {Co-Design of Anytime Computation and Robust Control},
      journal = {2015 IEEE Real-Time Systems Symposium (RTSS)},
      month = {December},
      year = {2015},
      abstract = {Control software of autonomous robots has
                stringent real-time requirements that must be met
                to achieve the control objectives. One source of
                variability in the performance of a control system
                is the execution time and accuracy of the state
                estimator that provides the controller with state
                information. This estimator is typically
                perception-based (e.g., Computer Vision-based) and
                is computationally expensive. When the
                computational resources of the hardware platform
                become overloaded, the estimation delay can
                compromise control performance and even stability.
                In this paper, we define a framework for
                co-designing anytime estimation and control
                algorithms, in a manner that accounts for
                implementation issues like delays and
                inaccuracies. We construct an anytime
                perception-based estimator from standard
                off-the-shelf Computer Vision algorithms, and show
                how to obtain a trade-off curve for its delay vs
                estimate error behavior. We use this anytime
                estimator in a controller that can use this trade-
                off curve at runtime to achieve its control
                objectives at a reduced energy cost. When the
                estimation delay is too large for correct
                operation, we provide an optimal manner in which
                the controller can use this curve to reduce
                estimation delay at the cost of higher inaccuracy,
                all the while guaranteeing basic objectives are
                met. We illustrate our approach on an autonomous
                hexrotor and demonstrate its advantage over a
                system that does not exploit co-design.},
      URL = {http://terraswarm.org/pubs/591.html}
  }
  

Posted by Barb Hoversten on 17 Jul 2015.
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